I use SAS to fit a simple mixed model where there is nested random effects of
Location like this:
proc mixed data = SAS_R_1; class Location Block Trt; model Adj = Location Trt Location*Trt; random Block(Location); run;
There is a lot of output but I focus mostly on the random effects covariance estimates:
Covariance Parameter Estimates Cov Parm Estimate Block(Location) 0.005619 Residual 0.03458
Then I try the same mode in R/lmer:
mymodel <- lmer(Adj ~ Location * Trt + (1|Location/Block), dt
but this raises a warning:
Warning messages: 1: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: large eigenvalue ratio - Rescale variables? 2: In as_lmerModLT(model, devfun) : Model may not have converged with 1 eigenvalue close to zero: 8.4e-10
Can I ignore this warning ?
In R my data looks like this:
Location Block Trt Adj 1 A 1 3 3.1645 2 A 1 4 3.1250 3 A 1 2 3.1594 4 A 1 1 3.2500 5 A 2 2 2.7130 6 A 2 1 3.2028
The full dataset is here: https://www.mediafire.com/file/afvgxc3y1xmekx9/SAS_R_1.csv/file
Any help will be very gratefully received.